AI Research Is Pushing The Limits Of What’s Possible

See the groundbreaking research with areas of focus in AI, self-driving cars, HPC, and more here at NVIDIA.

The most popular AI examples you learn about today – from voice-activated assistants to self-driving cars – were pioneered from research. Early AI research explored simple problem solving but ultimately paved the way for automation and formal reasoning that we see today such as natural language processing and image recognition. Now amplified with massive data volumes, complex algorithms, and better computing power, AI research is making it possible for today’s machines to learn in real-time and execute tasks that were previously thought impossible.

From training robots to generating realistic faces, here are three research works currently revolutionizing the world of AI technology:

1. Generating New Human Faces

In face-generating research, NVIDIA’s Finland research lab developed a method of training generative adversarial networks (GANs) that produced better results than existing techniques. These researchers demonstrated their success by applying it to the generation of realistic-looking human faces. The neural network creates new human faces by mixing characteristics like gender, hairstyle, and face shape in a multitude of ways. The project aims to pave the way for game developers to easily create realistic digital people as well as help those afflicted with prosopagnosia (face blindness), in which sufferers can’t recognize human faces.

The video shows the result of varying random celebrity facial characteristics, demonstrating an endless number of possible combinations.

2. Reconstructing Photos

Image inpainting, the task of filling in holes in an image, can be used in various applications. For instance, it can be used in image editing to delete unwanted content, while backfilling in the remaining space with related imagery. Researchers from NVIDIA expanded upon this by introducing a deep learning method inside a photo editing software that can handle gaps of any shape, size location, or distance from the image borders. Previous deep learning approaches have focused on rectangular areas located around the center of the image, and require expensive post-processing. With this new model, this team was the first to demonstrate the efficacy of deep learning image inpainting on irregularly shaped holes.

This video introduces a deep learning method that can edit images or reconstruct a corrupted image.

3. Robots Working Alongside Humans

Researchers from NVIDIA developed the first deep learning based system that can teach a robot to complete a task by just observing the actions of a human. By using GPUs, researchers were able to train a sequence of neural networks to perform tasks correlated with perception, program generation, and program execution. Thus, the robot learned the task from a single demonstration in the real world. With this success, the team plans to continue exploring the use of synthetic training data for robotics manipulation to enhance communication between humans and robots as well as enable people to work seamlessly alongside robots.

A human operator shows a pair of cubes to the robot and the system infers an appropriate program to correctly place the cube in the correct order.

These research innovations are only teasing the massive opportunities AI contains. Stay tuned for CVPR 2018, from June 19th – 21st, as we will be unveiling more groundbreaking AI projects aiming to further computer vision, robotics, self-driving, and more.